What are the business prospects for blockchain and big data? (part 3 of 3)

Fast, secure, transparent. These qualities define the technologies that are transforming both personal and business transactions.

One such technology, blockchain, enables the cryptocurrency Bitcoin. But blockchain’s potential value extends far beyond digital money. Several organizations are experimenting with blockchain’s possibilities for the global supply chain, everything from managing food fraud to ports and shipping.

Dr. Phyllis Schneck, currently the managing director of cyber solutions at Promontory, is a cybersecurity expert who will deliver a featured presentation on blockchain technology and another business enhancer – big data – at this year’s NDTA-USTRANSCOM Fall Meeting in St. Louis.

Schneck provided a brief overview of both topics during a conversation with DTJ. The following is the final part of the interview (part 1 is available here and part 2 is available here), and has been edited for length and clarity:

DTJ: I’m going to jump topics here a little. A buzzword that’s become more and more prevalent in looking at how companies manage and grow their business is “big data.” From a very high level perspective, how does the concept and application of big data play a role in business?

Dr. Schneck: I like to equate big data to the weather – there’s just so much data out there coming from weather satellites, balloons and human reporting around the world, we can’t use all of it. If we could use more of it, we’d be a lot smarter.

The concept of big data is the ability to go through more data and understand what’s related to what in our world, whether it’s weather or DNA or healthcare, to find things like trends and forecast tornados earlier (which is what I used to do in prior research) or find cures for diseases. Because all of a sudden you can see patterns that you’d never see before a computer could detect them. That is big data. IBM’s Watson is a groundbreaking capability with big data because it not only has the speed to ingest the data, it learns what matters to humans – and after all we are much more important than computers. Watson helps us use our human brains for the most important work by highlighting what may be tiny but tremendously significant data points within a heap that no human could have otherwise processed. That is going to change the world.

The other side to that is the need for big privacy. We have an enormous privacy practice in Promontory. We work very hard with privacy experts internally and externally, just as I did at the Department of Homeland Security, to make sure that, as we advise our clients, when they use this big data, they maintain everybody’s privacy to the best extent, and to the fullest extent of the law. So it’s a balance.

Where big data helps business a lot – to give you one example – is advertising. If you go online and look up something like recipes or kitchenware, for the next several weeks you will be getting ads for pots, and pans, and kitchen remodeling – anything that’s remotely related – because businesses are trying to track to whom they should target their expensive advertisements for the most likelihood that you will go buy something.

Big data obviously helps the weather forecasting, and I believe the Weather Channel is now part of IBM as well. My guess on that is largely it has to do with the ability for IBM’s high-speed processing and the Weather Channel’s amazing use of weather data, how you put that together. That’s what I first did in grad school, before I did cybersecurity. I worked on faster tornado modeling using Georgia Tech’s high-performance computing and real weather data we got from researchers at some of the national storm centers.

The ability to use high-speed computing to go through massive amounts of global data and discover things we could never have seen before – while preserving privacy – to me that’s the core, that’s the sweet spot of big data.

I think the word term big data is used too much. It really has been applied to everything, but when I think of big data, it’s what can you only do with a massive data set and computing that’s fast and smart enough – such as IBM’s Watson, for example – to really find those patterns.

This is an example of the powerful combination of Promontory and IBM, looking at how you help the financial sector find money laundering, a Promontory expertise, and now adding the ability with Watson to parse reams of log data that no human could ever detect on their own. And that’s one early example of how big data is helping business – and fighting crime!

Another great example is what Watson is doing for healthcare, and helping diagnose patients. This is not just standard artificial intelligence where it robotically goes through every possibility and finds the right move in the chess game. This is cognitive learning, where the computer is actually “trained” to understand ‘if I wasn’t right the first time, I’m not going to do it that way again.’

And it is taught things from regulations or it’s taught things from the medical books, and actually comes up with diagnoses for diseases – that are obviously double-checked by a human. But it saves people a lot of time, because no one human could read all of those books, no matter how good you are. And it’s able to get you to what’s important more quickly, and save our best and brightest human minds for what matters most.

DTJ: Have I missed any key points about blockchain, big data or related technologies that you want to discuss?

Dr. Schneck: I would just add that this is a very exciting time to be looking at these issues. It’s an exciting time to be at Promontory and IBM. In the context of your Fall Meeting, the responsibility of moving goods globally I think is enormous, and the addition of technology will help it. But we need to do it carefully. And I think that’s why it’s so important that we have these discussions with the people, especially members of the military, who have walked this walk and know the business. Technology exists to support them, and we are working to make it better for them every day.

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